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1

High Temperature and High Humidity Reduce the

Transmission of COVID-19

First draft: March 9, 2020

This draft: April 3, 2020

One Sentence Summary: High Temperature and High Humidity Reduce the

Transmission of COVID-19.

Abstract. This paper investigates the influence of air temperature and relative humidity on the transmission of COVID-19. After estimating the serial interval of COVID-19 from 105 hand-collected pairs of the virus carrier and the infected, we calculate the daily effective reproductive number, R, for each of all 100 Chinese cities with more than 40 cases. Using the daily R values from January 21 to 23, 2020 as proxies of non- intervened transmission intensity, we find, under a linear regression framework, high temperature and high humidity significantly reduce the transmission of COVID-19, respectively. One-degree Celsius increase in temperature and one percent increase in relative humidity lower R by 0.0225 and 0.0158, respectively. This result is consistent with the fact that the high temperature and high humidity reduce the transmission of influenza and SARS. It indicates that the arrival of summer and rainy season in the northern hemisphere can effectively reduce the transmission of the COVID-19. We also developed a website to provide R of major cities around the world according to their daily temperature and relative humidity: http://covid19-report.com/#/r-value

This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=3551767

2 Since December 2019, Wuhan, the capital of Hubei Province, China, has reported an outbreak of atypical pneumonia caused by COVID-19 (SARS-CoV-2 or 2019-nCov) (1, 2), the virus has transmitted nationwide and internationally (3-5). Compared with SARS, the range of the outbreak of COVID-19 is much wider. The transmission of coronaviruses can be affected by a number of factors, including climate conditions (such as temperature and humidity), population density and medical care quality (6, 7). Therefore, understanding the relationship between weather and the transmission of COVID-19 is the key to forecast the intensity and end time of this pandemic. Indirect evidence shows that up to March 22, 2020, 90% of COVID-19 cases have been recorded in non-tropical countries with low temperatures and low humidity; while much fewer cases are recorded in the tropics (8). However, up to now, there is no direct evidence on the influence of temperature and humidity on the transmission of the COVID-19. For example, on March 06, 2020, Michael Ryan, the executive director of the WHO Health Emergencies Program, said that people still did not know the activity or behavior of the COVID-19 virus in different climatic conditions (9). Our paper aims to provide direct evidence. Since the COVID-19 has spread widely to Chinese cities, and the intensity of transmission and weather conditions in these cities vary largely (Figure 1), we can, therefore, analyze the determinants of COVID-19 transmission, especially the weather factors. In order to formally quantify the transmission of COVID-19, we first fit 105 samples of serial intervals with the Weibull distribution (a distribution commonly used to fit the serial interval of influenza (10)). The mean and standard deviation of the serial interval are 7.4 and 5.2 days, respectively. With these numbers, we calculate the effective reproductive number, R, a quantity measuring the severity of infectiousness, for each of all 100 Chinese cities with more than 40 cases from the first-case date to February 20 by employing a time-dependent method (11). The inputs of the model are epidemic curves, i.e. the historical numbers of patients with symptom onset of each

day for a certain city. This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=3551767

3 Because we aim to study the influences of various factors on R under natural conditions,1 -scale intervention in the spread of COVID-19 on 24 January, when the first-level response to major public health emergencies in many major cities and provinces including Beijing and Shanghai are announced. Moreover, after the statement of person-to-person transmission from Professor Nanshan Zhong on the evening of January 20 through a public television interview, Chinese hospitals of all provinces began serious case recording of COVID-

19, we, therefore, take the daily R values from January 21 to January 23 to proxy the

non-intervened R for each city.2 Temperature, Relative Humidity and Effective Reproductive Numbers The WHO believes that coronavirus carriers are infectious 2 days before the onset of the symptoms (12), we, therefore, use three-day average temperature and relative humidity up to and including the day when the R-value is measured, respectively. Figure 1 shows the average R values from January 21 to 23 for different Chinese cities geographically. Compared with the southeast coast of China, cities in the northern area of China show relatively larger R values and lower temperatures and relative humidity. The scatter plots in Figure 2 illustrate two negative relations between the 3-day average temperature and R-value and between the 3-day average relative humidity and R-value, respectively. We then run a pooled cross-sectional regression of the daily R values of various cities on their 3-day average air temperature and relative humidity and control variables observed in 2018 including the GDP per capita, population density, number of hospital beds and the fraction of population over 65 in each city. We use White robust standard errors to adjust the t-statistics of the regression. Table 1 shows that the air temperature and relative humidity have a quite strong influence on R values with significance levels of 1% for all specifications. One-degree Celsius increase in temperature and one 1 2

This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=3551767

4 percent increase in relative humidity lower the R-value by 0.0225 and 0.0158, respectively. The control variables are not as significant as the temperature and relative humidity, but with expected signs. For example, cities with more hospital beds have a smaller transmission intensity, because the infected are treated in hospitals and hence unable to transmit to others. John Hopkins University has estimated a 5-day incubation period between exposure and symptoms (13), although we do not know whether the carriers are infectious in the whole incubation period, as a robustness check, we also use five-day average temperature and relative humidity up to and including the day when the R- value is measured, and rerun the regression. The last two columns in Table 1 show that temperature and relative humidity still have a strong influence on R values with a 1% significance level, consistent with the previous regression results. We then run a panel regression of daily R values on 3-day average temperatures, relative humidity and control variables with both fixed- and random-effects models. Temperature and relative humidity have quite strong influences on R values, with 1% significant levels for both in Table 2. Note that since control variables do not change on a daily basis from January 21 to 23, their effects are, therefore, absorbed in the fixed effects dummies in the fixed-effects panel regressions. We run a Hausman test with a null hypothesis that the random-effects model is preferred to the fixed-effects, and get -value less than 0.01, and therefore fixed-effects panel is preferred.

Absolute Humidity

Absolute humidity, the mass of water vapor per cubic meter of air, relates to both temperature and relative humidity. A previous work (14) shows that absolute humidity is a good solo variable explaining the seasonality of influenza. A significant negative relationship between absolute humidity and R-value is also shown in Figure 2. Panel A of Table 3 shows that consistent with (14), absolute humidity does out-perform the relative humidity (higher R2 and larger t-statistics) as a single variable in explaining

the cross-variation of the R values. This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=3551767

5 We then find a better variable from the absolute and relative humidity in explaining the variation of R values together with temperatures by performing a Davidson- MacKinnon Test (15). Test 1 of Panel B in Table 3 fails to accept that absolute humidity is better than relative humidity (t-stat of -0.2); however, Test 2 of Panel B in Table 3 shows that relative humidity is better than the absolute humidity with a 1% significance level (t-statistics -4.28). Overall, the Davidson-MacKinnon test shows that the model with relative humidity and temperature is better than the one with absolute humidity and temperature in explaining R values.

Worldwide COVID-19 Transmission Intensity

Assuming that the same relationship between temperature and relative humidity and R values (first column in Table 1) applies to cities outside China and that the temperature and relative humid of 2020 are the same as those in 2019, we can draw a map of R values for worldwide cities in Figure 3 by plugging the average March and July temperatures and relative humidity of 2019. This figure cautions people of the transmission risk of COVID-19 worldwide, in March and July of 2020, respectively. As expected, the R values are larger for temperate countries and smaller for tropical countries in March, which is consistent with the indirect evidence mentioned previously (8). In July, the arrival of summer and rainy season in the northern hemisphere can effectively reduce the transmission of the COVID-19.

Discussions

We find the high temperature and relative humidity reduce the transmission of COVID-19 both with 1% significance levels. This finding is consistent with the evidence that high temperature and high humidity reduce the transmission of influenza (14, 16-19), which can be explained by two possible reasons: First, the influenza virus is more stable in cold temperature, and respiratory droplets, as containers of viruses, remain airborne longer in dry air (20, 21). Second, cold and dry weather can also them more susceptible to the virus (22, 23).

These mechanisms are also likely to apply to the COVID-19 transmission. Our result This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=3551767

6 is also consistent with the evidence that high temperature and high relative humidity reduce the viability of SARS coronavirus (24, 25). Note that the R2 of our regression is about 20%, which means that 80% of R-value fluctuations cannot be explained by temperature and relative humidity (and controls). The three-day average temperatures and relative humidity in our sample range from -

21oC to 21oC and from 47 to 100, respectively, therefore it is still not known yet

whether these negative relationships between COVID-19 transmission and temperature and humidity still hold in extremely hot, cold, and dry areas. In the meanwhile, although our paper suggests that the arrival of summer and rainy season in the northern hemisphere can effectively reduce the transmission of the COVID-19, it is unlikely that the COVID-19 pandemic diminishes by summer since the central U.S., northwest China and countries in the southern hemisphere (e.g. Australia and South Africa) still have a high coronavirus transmission as shown in Figure 3. Therefore, other measures such as social distancing are still important for blocking the COVID-19 transmission. 7 (a) (b) (c) Figure 1: A city-level visualization of the COVID-19 transmission (a), temperature (b) and relative humidity (c). Average R values from January 21 to 23, 2020 for 100 Chinese cities are used in subplot (a). The average temperature and relative humidity for the same period are plotted in (b) and (c). Subplots (a), (b) and (c) together inform that the R values are larger in the cold and dry northern regions of China.

This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=3551767

8 (a) Effective reproduction number R v.s. temperature (b) Effective reproduction number R v.s. relative humidity

This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=3551767

9 (c) Effective reproduction number R v.s. absolute humidity Figure 2: Effective reproductive number R v.s. temperature, relative humidity and absolute humidity for 100 Chinese cities Daily R values from January 21 to 23 and temperature, relative humidity and absolute humidity averaged 3 days up to and including the day of R measurement are used in this figure. Negative relationships between temperature and R, relative humidity and R and absolute humidity and R are shown in (a), (b) and (c), respectively.

This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=3551767

10 (a) R values in March (b) R values in July Figure 3: Worldwide risks of COVID-19 outbreak in March and July 2020 We use coefficients from the first column of Table 1 to estimate R values of worldwide cities (represented by dots) for March and July 2020, where temperatures and relative humidity in 2019 are obtained from https://www.ncdc.noaa.gov/ and assumed to be the same as those of 2020.

This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=3551767

Table 1: Cross-sectional regression analysis

Daily R values from January 21 to 23 and temperature and relative humidity averaged over 3 and 5 days, respectively, up to and including the day when R is measured, are used in the regression for 100 Chinese cities with more than 40 cases. The regression is estimated by an Ordinary Least Square (OLS) method with White robust standard errors to adjust heteroskedasticity. T-statistics are in the italic format with *, ** and

Temperature -0.0233 -0.0225 -0.0269 -0.0271

t-statistics -3.96*** -4.33*** -3.75***

Relative Humidity

t-statistics -5.17*** -2.80*** -3.29***

GDP per Capita -0.0158

t-statistics -1.71*

Population Density 0.0821 0.0769

t-statistics 1.93* 1.82*

No. hospital beds -0.00246

t-statistics -2.43**

Percentage over 65 0.357

t-statistics 0.19 const 3.011 2.709 3.061 t-statistics 14.06*** 10.38*** 9.80*** 8.16***

R2 18% 14% 17%

This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=3551767

Table 2: Panel regression analysis

Daily R values from January 21 to 23 and temperature and relative humidity averaged over 3 and 5 days, respectively, up to and including the day when R is measured, are used in the regression for 100 Chinese cities with more than 40 cases. Fixed and random effects models are both performed with White robust standard errors to adjust heteroskedasticity. T-statistics are in the italic format with *, ** and ***

Temperature -0.0928 -0.0419 -0.204 -0.0553

t-statistics -6.48*** -7.75***

Relative Humidity

t-statistics -10.01*** -3.16***

GDP per Capita

t-statistics

Population Density 0.120 0.120

t-statistics 1.49 1.54

No. hospital beds -0.00481 -0.00443

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[PDF] [PDF] High Temperature and High Humidity Reduce the Transmission of